Monte Carlo Node Localization Based on Improved QUARTE Optimization

Abstract

Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

“…Song et al [19] presented a Monte Carlo node localizing method based on enhanced QUATRE optimization. Firstly, the model selects the higher quality node in the range of single hop of unknown node as temporary anchor node, and take the temporary anchor node and anchor node as the reference node to localization, for constructing a precise sampling region; Later, the enhance QUATRE optimization is employed for obtaining the calculated position of unknown node in the sampling region.…”

An effective QUATRE algorithm based on reorganized mechanism and its application for parameter estimation in improved photovoltaic module

“…Based on the data measured from the I-V characteristic curve, heuristic algorithms can estimate the parameter information inside the model more accurately. Some of the more commonly used algorithms are: Particle Swarm Optimization (PSO) [ 28 , 29 ], QUasi-Affine TRansformation Evolution Algorithm (QUATRE) [ 30 , 31 ], Pigeon-inspired Optimization (PIO) [ 16 ], Genetic Algorithm (GA) [ 32 ], Fish Migration Optimization (FMO) [ 33 ], Dragonfly Algorithm (DA) [ 34 , 35 ], Grey Wolf Optimization algorithm (GWO) [ 36 ], Artificial Bee Colony Algorithm (ABC) [ 37 ], Gannet optimization algorithm (GOA) [ 38 ], Colony Predation Algorithm (CPA) [ 39 , 40 ], Whale Optimization Algorithm (WOA) [ 41 ], Bamboo Forest Growth Optimization Algorithm (BFGO) [ 42 ], Water Cycle Algorithm (WCA) [ 43 ]. Heuristic algorithms have an advantage over traditional analytical and numerical calculation methods in that they do not rely on a specific choice of initial values and are known for providing higher accuracy in estimating parameters.…”

Elite Oppositional Farmland Fertility Optimization Based Node Localization Technique for Wireless Networks

“…The Monte Carlo node placement technique developed by Song et al [14] is based on the enhanced QUASI-Affine Transformation Evolutionary (QUATRE) algorithm and is described in detail below. After selecting the best general nodes within one hop of unidentified nodes as provisional ANs, and using the temporary AN and AN as orientation nodes to construct a more accurate sampling region, an improved QUATRE-optimized technique was used to obtain the evaluated position of unidentified nodes from the sampling area.…”